Recursive estimation in large panel data models: Theory and practice

Bai (2009) proposes recursive estimation for panel data models with interactive effects. We study the behaviours of this recursive estimator. The recursive formula is established that shows the behaviours of recursive estimators depend on the initial estimator, the population structure and the itera...

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Veröffentlicht in:Journal of econometrics Jg. 224; H. 2; S. 439 - 465
Hauptverfasser: Jiang, Bin, Yang, Yanrong, Gao, Jiti, Hsiao, Cheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.10.2021
Elsevier Sequoia S.A
Schlagworte:
Asymptotic methods
Central limit theorem
econometrics
Estimating techniques
Estimation
exhibitions
Factor model
Grammatical aspect
Initial estimation
Iterative methods
Longitudinal studies
Multiplicative structure
Nonlinear optimization
Panel data
population structure
Recursion
Recursive algorithms
Studies
Theorems
Initial estimation
Multiplicative structure
C33
Factor model
C13
Nonlinear optimization
ISSN:0304-4076, 1872-6895
Online-Zugang:Volltext
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Zusammenfassung:Bai (2009) proposes recursive estimation for panel data models with interactive effects. We study the behaviours of this recursive estimator. The recursive formula is established that shows the behaviours of recursive estimators depend on the initial estimator, the population structure and the iterative steps. Under some general scenarios, we find that the recursive estimator becomes consistent after the first iteration from any initials. We also obtain the optimal number of iterative steps under some prescribed conditions. The central limit theorem of the recursive estimator is established when the initial estimator is OLS. Various simulations are conducted to support our theoretical findings.
Bibliographie:ObjectType-Article-1
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ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2020.07.055